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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Ultra-Miniature Lidar Scanner for Launch Range Data Collection

The most critical component in lidar is its laser scanner, which delivers pulsed or CW laser to target with desirable field of view (FOV). Most existing lidars use a rotating or oscillating mirror for scanning, resulting in several drawbacks. A lidar scanning technology was developed that could achieve very high scanning speed, with an ultra-miniature size and much lighter weight. This technology promises at least a 10x performance improvement in these areas over existing lidar scanners. Features of the proposed ultra-miniature lidar scanner include the ability to make the entire scanner <2 mm in diameter; very high scanning speed (e.g. 5 - 20 kHz, in contrast to several hundred Hz in existing scanners); structure design to meet stringent requirements on size, weight, power, and compactness for various applications; and the scanning speed and FOV can be altered for obtaining high image resolutions of targeted areas and for diversified uses.

Geng, Jason↗
NASA NTRS2011

Estimation and Modeling of Enceladus Plume Density Using Attitude Control Data Collected by the Cassini Spacecraft During Low-Altitude Enceladus Flybys

The Cassini spacecraft was launched on 15 October 1997. After an interplanetary cruise of almost seven years, it arrived at Saturn on June 30, 2004. Major science objectives of the Cassini mission include investigations of the configuration and dynamics of Saturn's magnetosphere, the structure and composition of the rings, the characterization of several of Saturn's icy satellites, and Titan's atmosphere constituent abundance

Enceladus↗
NASA NTRS2020

Small Unmanned Aircraft System Off Nominal Operations Reporting System Unmanned Aircraft System: Traffic Management Technical Capability Level 4 Implementation, Data Collection and Analysis

NASA performed research and development of technologies and requirements for traffic management of small Unmanned Aircraft Systems (UAS). In this effort, a small UAS off-nominal situation reporting system was developed to capture information from off-nominal situations to understand their nature and reduce occurrences. This Technical Memorandum (TM) describes the reporting system and analysis of 116 off-nominal situation reports from 352 small UAS operations, which were conducted at two flight test ranges in Summer 2019.

Jung, Jaewoo↗
NASA NTRSDate not supplied

A New Statistical Estimate of the Radar Coverage of the Low Earth Orbit Debris Environment

For over three decades, the NASA Orbital Debris Program Office (ODPO) has used the Goldstone Orbital Debris Radar, Haystack Ultrawideband Satellite Imaging Radar (HUSIR), and Haystack Auxiliary (HAX) radar assets to collect data on the low Earth orbit (LEO) debris environment. Each radar, with its unique beamwidth, altitude and inclination coverage, and limiting size threshold, operates in a beam park mode to statistically sample the orbital debris population in LEO. Provided that these assets are shared with other users, the orbital debris data collection is not continuous; rather, intermittent data collects are acquired and sent to the NASA ODPO. To understand the sampling process conducted by each radar over time and any related observational biases, a Statistical Confirmation of Radar Uniformity or Bias (SCRUB) code has been implemented to model the coverage of these assets for informing future operations, as well as usage of the data collected from these ground-based sensors. For this analysis, Right Ascension of the Ascending Node (RAAN) is used as a metric to measure statistical coverage. A complete survey of the LEO environment is understood to be measurements that sufficiently sample all values of RAAN for each altitude-inclination pair visible from the radar asset. Regions of incompleteness or statistical bias can help inform future observation campaigns. The SCRUB tool evaluates the coverage of the LEO environment, not by examining individual objects that may pass through a sensor’s field of view (FOV), but by determining which orbit planes pass through the FOV. Once a pointing geometry for a radar site, observation time, and range extent are configured by the user, SCRUB computes the inclinations and altitudes that are visible by the sensor. For each inclination-altitude pair, there is a distinct pair of possible RAAN values, corresponding to the ascending and descending orbit passing through that point in the sensor cone. Repeating this process for all points in the beam, and for multiple time periods during an observation window, creates a matrix of all inclination-altitude-RAAN combinations that are visible during a sensor run. This process can then be repeated for all observations within a year (for an annual survey), propagating all the RAAN values to a common epoch, typically the start of a calendar year, and combining the observations to assemble a full estimate of the RAAN coverage of the LEO environment. This RAAN coverage can then be analyzed for uniformity of sampling, within a certain inclination-altitude pair, or between larger regions of the space environment. This paper provides a general overview of the radar assets utilized by ODPO, typical analysis data products assuming circular orbits, and a discussion of the algorithms that feed between modeling and measurement operations. Following the description of the algorithm, estimates of coverage using HUSIR radar data collected from multiple years are developed and compared.

Chris L Ostrom↗
NASA NTRS2000

Cavendish Balance Automation

This is the final report for a project carried out to modify a manual commercial Cavendish Balance for automated use in cryostat. The scope of this project was to modify an off-the-shelf manually operated Cavendish Balance to allow for automated operation for periods of hours or days in cryostat. The purpose of this modification was to allow the balance to be used in the study of effects of superconducting materials on the local gravitational field strength to determine if the strength of gravitational fields can be reduced. A Cavendish Balance was chosen because it is a fairly simple piece of equipment for measuring gravity, one the least accurately known and least understood physical constants. The principle activities that occurred under this purchase order were: (1) All the components necessary to hold and automate the Cavendish Balance in a cryostat were designed. Engineering drawings were made of custom parts to be fabricated, other off-the-shelf parts were procured; (2) Software was written in LabView to control the automation process via a stepper motor controller and stepper motor, and to collect data from the balance during testing; (3)Software was written to take the data collected from the Cavendish Balance and reduce it to give a value for the gravitational constant; (4) The components of the system were assembled and fitted to a cryostat. Also the LabView hardware including the control computer, stepper motor driver, data collection boards, and necessary cabling were assembled; and (5) The system was operated for a number of periods, data collected, and reduced to give an average value for the gravitational constant.

Thompson, Bryan↗
NASA NTRS2018

Observaciones de la Tierra desde la Estación Espacial Internacional

The International Space Station is a "global observation and diagnosis station." It promotes international Earth observations aimed at understanding and resolving the environmental issues of our home planet. A wide variety of Earth observation payloads can be attached to the exposed facilities on the station's exterior as well as within the Destiny module. The presence of a human crew also provides a unique capability for real-time observation of the Earth, and "on the fly" data collection using hand-held digital cameras, and the astronauts may also provide input to ground personnel programming the station's automated Earth observation systems. Several instruments are currently collecting data from the International Space Station; in addition, some instruments have completed their data collection missions, with other remote sensing systems in development or proposed by researchers from the partner countries, NASA, academic institutions, and corporations. The existing international partnerships, fundamental to the International Space Station, facilitate data sharing that can benefit people around the world and promote international collaboration on other Earth observation activities. The station contributes to humanity by collecting data on global climate, environmental change, and natural hazards using its unique complement of crew-operated and automated Earth observation payloads.

Sotomayor, Jorge L.↗
NASA NTRS1973

Detection of turbidity dynamics in Tampa Bay, Florida using multispectral imagery from ERTS-1

In 1970, Congress authorized the deepening of the Tampa Bay channel (Rivers and Harbors Act of 1970) from 34 to 44 feet. In order to determine the effects of this deepening on circulation, water quality, and biota, during and after the construction, the U.S. Geological Survey, in cooperation with the Tampa Port Authority, has collected data and developed a digital simulation model of the bay. In addition to data collected using conventional tools, use is being made of data collected from ERTS-1. Return beam vidicon (RBV) multispectral data were collected, while a shell dredging barge was operating in the bay, and used for turbidity recognition and unique spectral signatures representative of type and amount of material in suspension. A three-dimensional concept of the dynamics of the plume was achieved by superimposing the parts of the plume recognized in each RBV band. This provides a background for automatic computer processing of ERTS data and three-dimensional modeling of turbidity plumes.

Coker, A. E.↗
NASA NTRS2007

Establishing a Near Term Lunar Farside Gravity Model via Inexpensive Add-on Navigation Payload

The Space Communications and Navigation, Constellation Integration Project (SCIP) is tasked with defining, developing, deploying and operating an evolving multi-decade communications and navigation (C/N) infrastructure including services and subsystems that will support both robotic and human exploration activities at the Moon. This paper discusses an early far side gravitational mapping service and related telecom subsystem that uses an existing spacecraft (WIND) and the Lunar Reconnaissance Orbiter (LRO) to collect data that would address several needs of the SCIP. An important aspect of such an endeavor is to vastly improve the current lunar gravity model while demonstrating the navigation and stationkeeping of a relay spacecraft. We describe a gravity data acquisition activity and the trajectory design of the relay orbit in an Earth-Moon L2 co-linear libration orbit. Several phases of the transfer from an Earth-Sun to the Earth-Moon region are discussed along with transfers within the Earth-Moon system. We describe a proposed, but not integrated, add-on to LRO scheduled to be launched in October of 2008. LRO provided a real host spacecraft against which we designed the science payload and mission activities. From a strategic standpoint, LRO was a very exciting first flight opportunity for gravity science data collection. Gravity Science data collection requires the use of one or more low altitude lunar polar orbiters. Variations in the lunar gravity field will cause measurable variations in the orbit of a low altitude lunar orbiter. The primary means to capture these induced motions is to monitor the Doppler shift of a radio signal to or from the low altitude spacecraft, given that the signal is referenced to a stable frequency reference. For the lunar far side, a secondary orbiting radio signal platform is required. We provide an in-depth look at link margins, trajectory design, and hardware implications. Our approach posed minimum risk to a host mission while maintaining a very low implementation and operations cost.

Folta, David↗
NASA NTRS2011

Collaborative Clustering for Sensor Networks

Traditionally, nodes in a sensor network simply collect data and then pass it on to a centralized node that archives, distributes, and possibly analyzes the data. However, analysis at the individual nodes could enable faster detection of anomalies or other interesting events, as well as faster responses such as sending out alerts or increasing the data collection rate. There is an additional opportunity for increased performance if individual nodes can communicate directly with their neighbors. Previously, a method was developed by which machine learning classification algorithms could collaborate to achieve high performance autonomously (without requiring human intervention). This method worked for supervised learning algorithms, in which labeled data is used to train models. The learners collaborated by exchanging labels describing the data. The new advance enables clustering algorithms, which do not use labeled data, to also collaborate. This is achieved by defining a new language for collaboration that uses pair-wise constraints to encode useful information for other learners. These constraints specify that two items must, or cannot, be placed into the same cluster. Previous work has shown that clustering with these constraints (in isolation) already improves performance. In the problem formulation, each learner resides at a different node in the sensor network and makes observations (collects data) independently of the other learners. Each learner clusters its data and then selects a pair of items about which it is uncertain and uses them to query its neighbors. The resulting feedback (a must and cannot constraint from each neighbor) is combined by the learner into a consensus constraint, and it then reclusters its data while incorporating the new constraint. A strategy was also proposed for cleaning the resulting constraint sets, which may contain conflicting constraints; this improves performance significantly. This approach has been applied to collaborative clustering of seismic and infrasonic data collected by the Mount Erebus Volcano Observatory in Antarctica. Previous approaches to distributed clustering cannot readily be applied in a sensor network setting, because they assume that each node has the same view of the data set. A view is the set of features used to represent each object. When a single data set is partitioned across several computational nodes, distributed clustering works; all objects have the same view. But when the data is collected from different locations, using different sensors, a more flexible approach is needed. This approach instead operates in situations where the data collected at each node has a different view (e.g., seismic vs. infrasonic sensors), but they observe the same events. This enables them to exchange information about the likely cluster membership relations between objects, even if they do not use the same features to represent the objects.

Wagstaff. Loro :/↗
NASA NTRS2020

Performance Assessments of the SNPP and N20 VIIRS DNB Using Observations of Bright Stars

The Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the Suomi-NPP (SNPP) and NOAA-20 (N20) spacecrafts is a multi-spectral Earth-observing instrument with spectral channels ranging from the visible to the long-wave infrared. In addition to these spectral channels, the VIIRS instrument features a panchromatic Day/Night band (DNB) with 3 gain stages (low, mid, and high) which allows for Earth-view (EV) radiance retrievals over 7 orders of magnitude. The DNB is able to collect data ranging from dim city lights during nighttime up to reflected sunlight from cloud-tops during daytime. The DNB data collection is divided into 32 aggregation modes, where sub-pixel aggregation is used to maintain a nearly constant pixel footprint on ground during EV data collection throughout each scan. The DNB is also sensitive enough to observe bright stars through the instrument space-view (SV) port. Data from these bright stars can be used to track the temporal stability of the sensor gain and as a method of intercomparison between the two VIIRS instruments. In this work, we will present several sensor performance assessments of the VIIRS DNB using stars. In addition to the sensor gain trending, these assessments will include a comparison of the differences between the daytime and nighttime data due to the effects of stray-light, a comparison of the measured signal as a function of aggregation mode, a comparison between the two high-gain sub-stages, and half-angle mirror side for both the SNPP and N20 VIIRS DNB.

VIIRS↗
NASA NTRS1988

Determination of the Venus flyby orbits of the Soviet Vega probes using VLBI techniques

In December 1984, the Soviet Union launched two identical Vega spacecraft with the dual objective of exploring Venus and continuing to rendezvous with the comet Halley. The two Vega spacecraft encountered Venus in mid-June 1985 and successfully deployed entry probes and wind-measuring balloons into the Venus atmosphere. An objective of the Venus Balloon experiment was to measure the Venus winds using differential VLBI from the balloon and the flyby bus. NASA's Deep Space 64 meter subnet was part of a world wide network organized to collect data from the Vega probes and balloons. A critical element of this experiment was an accurate determination of the Venus relative flyby orbits of the Vega spacecraft during the 46 hour balloon lifetime. Venus flyby solutions were independently determined by the Soviets using two-way range and Doppler from Soviet stations and by JPL using one-way Doppler and VLBI data collected from the DSN. The Vega flyby solutions determined by the Soviets using a sparse two-way tracking strategy with JPL solutions using the DSN VLBI data to complement the Soviet data and with solutions using only one-way data collected by the DSN were compared.

Ellis, J.↗
NASA NTRS2014

A Preliminary Analysis of Precipitation Properties and Processes during NASA GPM IFloodS

The Iowa Flood Studies (IFloodS) is a NASA Global Precipitation Measurement (GPM) ground measurement campaign, which took place in eastern Iowa from May 1 to June 15, 2013. The goals of the field campaign were to collect detailed measurements of surface precipitation using ground instruments and advanced weather radars while simultaneously collecting data from satellites passing overhead. Data collected by the radars and other ground instruments, such as disdrometers and rain gauges, will be used to characterize precipitation properties throughout the vertical column, including the precipitation type (e.g., rain, graupel, hail, aggregates, ice crystals), precipitation amounts (e.g., rain rate), and the size and shape of raindrops. The impact of physical processes, such as aggregation, melting, breakup and coalescence on the measured liquid and ice precipitation properties will be investigated. These ground observations will ultimately be used to improve rainfall estimates from satellites and in particular the algorithms that interpret raw data for the upcoming GPM mission's Core Observatory satellite, which launches in 2014. The various precipitation data collected will eventually be used as input to flood forecasting models in an effort to improve capabilities and test the utility and limitations of satellite precipitation data for flood forecasting. In this preliminary study, the focus will be on analysis of NASA NPOL (S‐band, polarimetric) radar (e.g., radar reflectivity, differential reflectivity, differential phase, correlation coefficient) and NASA 2D Video Disdrometers (2DVDs) measurements. Quality control and processing of the radar and disdrometer data sets will be outlined. In analyzing preliminary cases, particular emphasis will be placed on 1) documenting the evolution of the rain drop size distribution (DSD) as a function of column melting processes and 2) assessing the impact of range on ground‐based polarimetric radar estimates of DSD properties.

Carey, Lawrence↗
NASA NTRS1973

Analysis of partial-reflection data from the solar eclipse of 10 Jul. 1972

Partial-reflection data collected for the eclipse of July 10, 1972 as well as for July 9 and 11, 1972, are analyzed to determine eclipse effects on D-region electron densities. The partial-reflection experiment was set up to collect data using an on-line PDP-15 computer and DECtape storage. The electron-density profiles show good agreement with results from other eclipses. The partial-reflection programs were changed after the eclipse data collection to improve the operation of the partial-reflection system. These changes were mainly due to expanded computer hardware and have simplified the operations of the system considerably.

Bean, T. A.↗